Run: 2026-02-19-01KHSZ1CQD6J0ZCP0J0889SYKY

Run Details

Duration:
28.2s
Cost:
$0.001627
Provider:
DeepInfra

Model input

System
You are an expert fiction writer. Always keep the following rules in mind: - Write in past tense and use General English spelling, grammar, and colloquialisms/slang. - Write in active voice - Always follow the "show, don't tell" principle. - Avoid adverbs and cliches and overused/commonly used phrases. Aim for fresh and original descriptions. - Convey events and story through dialogue. - Mix short, punchy sentences with long, descriptive ones. Drop fill words to add variety. - Skip "he/she said said" dialogue tags and convey people's actions or face expressions through their speech - Avoid mushy dialog and descriptions, have dialogue always continue the action, never stall or add unnecessary fluff. Vary the descriptions to not repeat yourself. - Put dialogue on its own paragraph to separate scene and action. - Reduce indicators of uncertainty like "trying" or "maybe" When writing text: - NEVER conclude the scene on your own, follow the beat instructions very closely. - NEVER end with foreshadowing. - NEVER write further than what I prompt you with. - AVOID imagining possible endings, NEVER deviate from the instructions. - STOP EARLY if the continuation contains what was required in the instructions. You do not need to fill out the full amount of words possible.
User/Human
Take into account the following glossary of characters/locations/items/lore... when writing your response: <codex> <character id="4416" name="Detective Harlow Quinn" aliases="Quinn, Detective Quinn, Harlow" pronouns="she/her" species="Human" storyRole="Antagonist"> <eyeColor>brown</eyeColor> <hair>closely cropped salt-and-pepper</hair> <height>5'9"</height> <age>41</age> <jawline>sharp jaw</jawline> <watch>worn leather watch on her left wrist</watch> <bearing>military precision</bearing> <description>A Metropolitan Police detective with 18 years of decorated service who suspects the clique is involved in criminal activity.</description> <backstory>She lost her partner, DS Morris, during a case three years ago under unexplained circumstances that had supernatural origins she doesn't yet understand.</backstory> </character> <character id="4414" name="Eva Kowalski" aliases="Eva, Evie" pronouns="she/her" species="Human" storyRole="Best Friend"> <eyeColor>green</eyeColor> <hair>curly red</hair> <height>5'4"</height> <age>26</age> <glasses>round glasses</glasses> <satchel>worn leather satchel full of books</satchel> <freckles>freckled complexion</freckles> <nervousHabit>tucking hair behind her left ear</nervousHabit> <description>Aurora's childhood friend and an occult researcher.</description> <backstory>Moved to London two years before Aurora and works as a research assistant at the British Museum's restricted archives. She holds a master's degree in Ancient History from Oxford.</backstory> </character> <location id="856" name="The Veil Market" aliases="Veil Market, the Market, underground market" realm="Earth"> <currentLocation>abandoned Tube station beneath Camden</currentLocation> <entryRequirement>bone token</entryRequirement> <schedule>moves locations every full moon</schedule> <description>A hidden supernatural black market that sells enchanted goods, banned alchemical substances, and information.</description> </location> <item id="2004" name="Veil Compass" aliases="the Compass, shadow compass" type="Tool" origin="The Veil Market"> <casingMaterial>small brass compass</casingMaterial> <faceMarkings>face etched with protective sigils</faceMarkings> <needle>points toward the nearest supernatural rift or portal</needle> <patina>casing has a patina of verdigris</patina> <creator>crafted by a Shade artisan</creator> <description>A compass attuned to supernatural energy.</description> </item> </codex> Continue the story using the following instructions: <instructions> Write a fully fleshed out scene of at least 1000 words in the mystery genre. A detective arrives at a crime scene in an unusual location. Something is wrong — the evidence doesn't add up. A colleague is present, offering a different interpretation. Through careful observation and deduction, the detective begins to see what others have missed. Use the codex entries above as your world reference for characters and setting. Only include the prose of the scene without any markdown or commentary. </instructions>

Model response

AI
Detective Harlow Quinn ducked under the yellow police tape stretched across the entrance to the abandoned Tube station. The air hung heavy with the stench of mildew and decay. Her footsteps echoed off the tiled walls as she descended deeper into the tunnels, broken only by the intermittent buzz of the flickering fluorescent lights. In the distance, a faint glow and the murmur of voices drew her forward. She emerged onto a forgotten platform where a small crowd had gathered. Uniformed officers milled about, their faces ashen, their voices hushed. At the center of the platform, a crumpled form lay in a pool of crimson. Quinn's sharp gaze swept the scene, absorbing every detail. Eva Kowalski stood off to one side, her round glasses reflecting the harsh light. She clutched a worn leather satchel to her chest, her knuckles white. She glanced up as Quinn approached, tucking a strand of curly red hair behind her ear with a shaking hand. "Harlow," Eva said, her voice tight. "I—I didn't expect to see you here." Quinn cocked an eyebrow. "This is a crime scene, Eva. What are you doing here?" Eva hesitated, her eyes darting to the body. "I was meeting someone. About a... a research project." Quinn followed her gaze, taking in the victim's sprawled limbs, the dark stains on the concrete. She crouched down, examining the wounds. "These look like claw marks," she muttered. "But there are no animal tracks. No signs of a struggle." Eva shifted nervously, hugging her satchel tighter. "There's something else," she said quietly. "Something the others didn't notice." Quinn stood, fixing Eva with a penetrating stare. "What do you mean?" Eva bit her lip, then reached into her satchel and pulled out a small brass compass. The casing was etched with strange symbols, the needle quivering. "This is a Veil Compass," she said. "It's attuned to supernatural energy." Quinn's eyes narrowed. "Supernatural," she repeated flatly. Eva nodded, her freckled face pale. "The needle points to the nearest rift or portal. I think... I think that's what the victim stumbled upon." Quinn took the compass, turning it over in her gloved hands. The needle spun erratically, then settled, pointing down the darkened tunnel. A muscle worked in Quinn's jaw as she handed it back to Eva. "I lost my partner to something I couldn't explain," she said, her voice low. "I won't let that happen again." She strode off down the tunnel, her footsteps echoing in the gloom. Eva hurried after her, the compass in her trembling hands. The shadows closed in around them as they descended deeper into the unknown.
created_at"2026-02-19T03:28:00.247Z" 2/19/2026, 3:28:00 AM
model"nousresearch/hermes-3-llama-3.1-405b"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency433
moderation_latency(null)
generation_time28144
tokens_prompt1184
tokens_completion672
native_tokens_prompt1027
native_tokens_completion600
native_tokens_completion_images(null)
native_tokens_reasoning0
native_tokens_cached0
num_media_prompt(null)
num_input_audio_prompt(null)
num_media_completion0
num_search_results(null)
origin"https://ncbench.com/"
is_byokfalse
finish_reason"stop"
native_finish_reason"stop"
usage0.001627
router(null)
provider_responses
0
endpoint_id"3280e8f8-0960-400c-8284-2e03a0bcb446"
id"cmpl-93643607a321497c9e9d7e24b77382d6"
is_byokfalse
latency74
model_permaslug"nousresearch/hermes-3-llama-3.1-405b"
provider_name"DeepInfra"
status200
api_type"completions"
id"gen-1771471680-YG6kiZTfyjw7YmMX69bi"
upstream_id"cmpl-93643607a321497c9e9d7e24b77382d6"
total_cost0.001627
cache_discount(null)
upstream_inference_cost0
provider_name"DeepInfra"

Evaluation details

Result Evaluator Details Meta Data
0.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags6
adverbTagCount2
adverbTags
0"she said quietly [quietly]"
1"she repeated flatly [flatly]"
dialogueSentences15
tagDensity0.4
leniency0.8
rawRatio0.333
effectiveRatio0.267
88.53% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount436
totalAiIsmAdverbs1
found
0
adverb"nervously"
count1
highlights
0"nervously"
100.00% AI-ism character names
Target: 0 AI-default names (17 tracked, −20% each)
codexExemptions(empty)
found(empty)
100.00% AI-ism location names
Target: 0 AI-default location names (33 tracked, −20% each)
codexExemptions(empty)
found(empty)
19.72% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount436
totalAiIsms7
found
0
word"footsteps"
count2
1
word"echoed"
count1
2
word"eyebrow"
count1
3
word"etched"
count1
4
word"echoing"
count1
5
word"gloom"
count1
highlights
0"footsteps"
1"echoed"
2"eyebrow"
3"etched"
4"echoing"
5"gloom"
100.00% Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches1
maxInWindow1
found
0
label"eyes widened/narrowed"
count1
highlights
0"eyes narrowed"
100.00% Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells0
narrationSentences33
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount0
narrationSentences33
filterMatches(empty)
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences42
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
maxSentenceWordsSeen25
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords435
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions6
unquotedAttributions0
matches(empty)
15.27% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions21
wordCount334
uniqueNames5
maxNameDensity2.69
worstName"Quinn"
maxWindowNameDensity4.5
worstWindowName"Eva"
discoveredNames
Harlow1
Quinn9
Tube1
Kowalski1
Eva9
persons
0"Harlow"
1"Quinn"
2"Kowalski"
3"Eva"
places(empty)
globalScore0.153
windowScore0.167
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences25
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount435
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences42
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs15
mean29
std16.25
cv0.56
sampleLengths
068
146
246
313
415
517
640
718
812
938
107
1125
1235
1320
1435
94.63% Passive voice overuse
Target: ≤2% passive sentences
passiveCount1
totalSentences33
matches
0"was etched"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs66
matches(empty)
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences42
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount334
adjectiveStacks0
stackExamples(empty)
adverbCount7
adverbRatio0.020958083832335328
lyAdverbCount6
lyAdverbRatio0.017964071856287425
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences42
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences42
mean10.36
std4.82
cv0.466
sampleLengths
018
111
225
314
412
510
615
79
814
912
1020
116
127
134
1411
158
169
1716
186
197
2011
217
226
235
248
254
2616
2710
287
295
303
314
326
3319
3411
3511
3613
3714
386
3912
4010
4113
74.60% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats2
diversityRatio0.47619047619047616
totalSentences42
uniqueOpeners20
0.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount0
totalSentences31
matches(empty)
ratio0
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount9
totalSentences31
matches
0"Her footsteps echoed off the"
1"She emerged onto a forgotten"
2"She clutched a worn leather"
3"She glanced up as Quinn"
4"She crouched down, examining the"
5"she said quietly"
6"she repeated flatly"
7"she said, her voice low"
8"She strode off down the"
ratio0.29
8.39% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount28
totalSentences31
matches
0"Detective Harlow Quinn ducked under"
1"The air hung heavy with"
2"Her footsteps echoed off the"
3"She emerged onto a forgotten"
4"Quinn's sharp gaze swept the"
5"Eva Kowalski stood off to"
6"She clutched a worn leather"
7"She glanced up as Quinn"
8"Eva said, her voice tight"
9"Quinn cocked an eyebrow."
10"Eva hesitated, her eyes darting"
11"Quinn followed her gaze, taking"
12"She crouched down, examining the"
13"Eva shifted nervously, hugging her"
14"she said quietly"
15"Quinn stood, fixing Eva with"
16"Eva bit her lip, then"
17"The casing was etched with"
18"Quinn's eyes narrowed."
19"she repeated flatly"
ratio0.903
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences31
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences19
technicalSentenceCount0
matches(empty)
0.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags6
uselessAdditionCount2
matches
0"Eva said, her voice tight"
1"she said, her voice low"
16.67% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags6
fancyCount2
fancyTags
0"she muttered (mutter)"
1"she repeated flatly (repeat)"
dialogueSentences15
tagDensity0.4
leniency0.8
rawRatio0.333
effectiveRatio0.267
73.9271%